| title | Building to the Test: Coding Agents Deliver What You Check, Not What You Requested |
|---|---|
| authors | Yanuo Ma, Ben Kereopa-Yorke, Ben Schultz |
| date | 2026-06-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.28430 |
| affiliation | Microsoft |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Benchmarks are widely used to evaluate task completion by Large Language Models (LLMs), but this approach has accumulated construction-validity problems, and a passing score may not show whether the requested task was delivered. We study both problems. In a controlled code-as-spec setup, two production Copilot CLI agents (claude-opus-4.7, gpt-5.5) re-implement a React Fluent-UI data table in Angular as a reusable library under a hidden 222-test Playwright oracle across 18 runs and three oracle-availability conditions. Alongside the score, we run a mechanical library audit and check each verdict with a no-op ablation. Without the oracle, the library is present but unfinished, revealed by scores. With the oracle in the loop, the score reaches near-perfect, but from a demo holding the tested behavior directly, the library left dead or absent. We call this building to the test; the broader disposition behind both we call validation self-awareness. The agent does not, on its own, validate what it ships as a user would. Prevalence remains an open question across other agents, signals, and model families. Beyond benchmark scores, dispositions like validation self-awareness merit research attention. |
cite
@misc{ma2026building,
title={Building to the Test: Coding Agents Deliver What You Check, Not What You Requested},
author={Yanuo Ma and Ben Kereopa-Yorke and Ben Schultz},
year={2026},
eprint={2606.28430},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.28430}
}